Contributions to Real-time Metric Localisation with Wearable Vision Systems

Q4 Computer Science
D. Gutiérrez
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Abstract

Under the rapid development of electronics and computer science in the last years, cameras have become omnipresent nowadays, to such extent that almost everybody is able to carry one at all times embedded into their cellular phone. What makes cameras specially appealing for us is their ability to quickly capture a lot of information of the environment encoded in one image or video, allowing us to immortalize special moments inour life or share reliable visual information of the environment with other persons. However, while the task ofextracting the information from an image may by trivial for us, in the case of computers complex algorithmswith a high computational burden are required to transform a raw image into useful information. In this sense, the same rapid development in computer science that allowed the widespread of cameras has enabled also the possibility of real-time application of previously practically infeasible algorithms. Among the current fields of research in the computer vision community, this thesis is specially concerned in metric localisation and mapping algorithms. These algorithms are a key component in many practical applications such as robot navigation, augmented reality or reconstructing 3D models of the environment. The goal of this thesis  is to delve into visual localisation and mapping from vision, paying special attention to conventional and unconventional cameras which can be easily worn or handled by a human. In this thesis I contribute in the following aspects of the visual odometry and SLAM (Simultaneous Localisation and Mapping) pipeline: - Generalised Monocular SLAM for catadioptric central cameras - Resolution of the scale problem in monocular vision - Dense RGB-D odometry - Robust place recognition - Pose-graph optimisation
对可穿戴视觉系统实时度量定位的贡献
近年来,随着电子技术和计算机科学的飞速发展,相机已经无处不在,以至于几乎每个人都可以随时将其嵌入手机中。相机之所以对我们特别有吸引力,是因为它能够快速捕捉到大量编码在一张图像或视频中的环境信息,使我们能够永远记住生活中的特殊时刻,或者与其他人分享可靠的环境视觉信息。然而,虽然从图像中提取信息的任务对我们来说可能是微不足道的,但在计算机的情况下,将原始图像转换为有用的信息需要复杂的算法和高计算负担。从这个意义上说,计算机科学的快速发展使得相机的普及也使得以前实际上不可行的算法的实时应用成为可能。在当前计算机视觉界的研究领域中,本文特别关注度量定位和映射算法。这些算法是许多实际应用中的关键组成部分,例如机器人导航,增强现实或重建环境的3D模型。本文的目标是深入研究视觉定位和视觉映射,特别关注传统和非传统的相机,这些相机可以很容易地由人类佩戴或处理。在本文中,我在视觉里程计和SLAM(同步定位和映射)管道的以下方面做出了贡献:-反射中心相机的广义单眼SLAM -解决单眼视觉中的比例问题-密集RGB-D里程计-鲁棒位置识别-姿态图优化
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Electronic Letters on Computer Vision and Image Analysis
Electronic Letters on Computer Vision and Image Analysis Computer Science-Computer Vision and Pattern Recognition
CiteScore
2.50
自引率
0.00%
发文量
19
审稿时长
12 weeks
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